ArticleScientific reports2024
Role of peritumoral tissue analysis in predicting characteristics of hepatocellular carcinoma using ultrasound-based radiomics.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Intra-ablational and peri-ablational radiomics based on post-RFA gadoxetic acid disodium-enhanced MRI for predicting short-term prognosis in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- A novel deep learning and radiomics approach based on DCE-MRI for predicting the P53 mutation status in hepatocellular carcinoma.Journal of gastrointestinal oncology · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Pediatric Personalized Deep Learning Models for Segmentation of Hepatoblastoma at CT and MRI.Radiology. Imaging cancer · 2026Article
- MRI Radiomics for Distinguishing HBV-Related Early Hepatocellular Carcinoma from Precancerous Nodules.Journal of hepatocellular carcinoma · 2026Article
- Peritumoral bone changes in mandibular gingival squamous cell carcinoma: A prospective radiologic-histopathologic correlation study.Journal of dental sciences · 2026Article
- Emerging non-invasive imaging biomarkers of Ki-67 in pancreatic cancer: Toward predictive precision oncology.World journal of gastrointestinal oncology · 2025Article
- Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges.World journal of gastroenterology · 2025Review
- Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study.World journal of gastrointestinal oncology · 2025Article
- Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features.World journal of gastrointestinal oncology · 2025Article
- Comparative analysis of deep learning and radiomics models in predicting hepatocellular carcinoma differentiation via ultrasound.Frontiers in medicine · 2025Article
- Integrating intratumoral, peritumoral, and clinical features in an ultrasound-based radiomics model: contributions and synergies for predicting microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Identification of endogenous reference genes for RT-qPCR analysis in breast cancer and matched adjacent tissues.Frontiers in oncology · 2025Article
- A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography.BMC cancer · 2024Article
- Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis.Journal of hepatocellular carcinoma · 2024Review
- An MRI-Based Intratumoral and Peritumoral 2.5D Deep Learning Model for Predicting P53-Mutated Hepatocellular Carcinoma: A Two-Center Study.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Predicting the biological characteristics of hepatocellular carcinoma (HCC) is essential for personalized treatment. This study explored the role of ultrasound-based radiomics of peritumoral tissues for predicting HCC features, focusing on differentiation, cytokeratin 7 (CK7) and Ki67 expression, and p53 mutation status. A cohort of 153 patients with HCC underwent ultrasound examinations and radiomics features were extracted from peritumoral tissues. Subgroups were formed based on HCC characteristics. Predictive modeling was carried out using the XGBOOST algorithm in the differentiation subgroup, logistic regression in the CK7 and Ki67 expression subgroups, and support vector machine learning in the p53 mutation status subgroups. The predictive models demonstrated robust performance, with areas under the curves of 0.815 (0.683-0.948) in the differentiation subgroup, 0.922 (0.785-1) in the CK7 subgroup, 0.762 (0.618-0.906) in the Ki67 subgroup, and 0.849 (0.667-1) in the p53 mutation status subgroup. Confusion matrices and waterfall plots highlighted the good performance of the models. Comprehensive evaluation was carried out using SHapley Additive exPlanations plots, which revealed notable contributions from wavelet filter features. This study highlights the potential of ultrasound-based radiomics, specifically the importance of peritumoral tissue analysis, for predicting HCC characteristics. The results warrant further validation of peritumoral tissue radiomics in larger, multicenter studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.